Traffic wave

About: Traffic wave is a research topic. Over the lifetime, 2106 publications have been published within this topic receiving 62117 citations. The topic is also known as: phantom traffic jam & ghost jams.

Deriving features of traffic flow around an intersection from trajectories of vehicles

Abstract: Features of traffic flow, such as travel time, traffic volume, road choice, etc., are always important information for traffic planning and management. Conventional methods of collecting traffic flow features include loop detectors, closed-circuit televisions, field investigation, etc. They record vehicles' mobility in an indirect and infrastructure-based manner, and have difficulties in producing road choice data of vehicles or cover the whole road network. On the other hand, the advances in positioning and communication technologies make it possible to collect trajectories of moving vehicles. The collected trajectories can record the above information in a ‘natural’, direct, and vehicle-based manner. So far, various applications of location-based services have been implemented and meanwhile, a large number of vehicle trajectories have been collected. Therefore, it is possible to bring trajectories of vehicles into traffic study as an important supplement to conventional traffic data. To date, several fundamental works have been conducted to handle vehicle trajectories, such as map-matching algorithms, trajectory data representation, and trajectory data indexing and query language, etc. Based on them, the paper aims at illustrating a practical application of trajectories in analyzing traffic flow around a traffic intersection. A typical intersection in Shanghai with heavy traffic is selected as an example. More than 10GB trajectory data collected with GPS receivers of about 2000 taxis in Shanghai for 6 days is obtained. All trips around the intersection are extracted first. Then, a map-matching algorithm is employed to represent each trip as a journey record, i.e. a network route consisting of road segments and the intersection. Third, all journey records are classified into several groups according to their turning directions around the intersection, and then, specific analyses can be made. Since temporal information is inherent in each trip, all these analyses are time-dependant, such as to summarize the number of vehicles turning left or the average time for vehicles going straight across the intersection from a certain road segment during rush hours. In our case study, coupling the analyzing results with the surrounding characteristics of the intersection, we have tried to figure out and explain various patterns behind traffic flow. This example illustrates the potential of applying vehicle trajectories to traffic study.

Formation and Propagation of Local Traffic Jam

Abstract: Large scale traffic congestion often stems from local traffic jam in single road or intersection. In this paper, macroscopic method was used to explore the formation and propagation of local traffic jam. It is found that (1) the propagation of traffic jam can be seen as the propagation of traffic signal parameters, that is, virtual split and virtual green time; (2) for a road with endogenous flow, entrance location influences the jam propagation. With the same demand (upstream links flow and entrance flow), the upstream got more influence; (3) when a one-lane road is thoroughly congested, virtual signal parameters everywhere are the same as that at stop line; for a basic road, the virtual signals work in a cooperative manner; (4) phase sequence is one important parameter that influences traffic performances during peak hour where spill back of channelization takes place. The same phase plan for left-turn flow and through flow would be preferred; (5) signal coordination plays an important role in traffic jam propagation and hence effective network signal parameters should be designed to prevent jam from propagation to the whole network. These findings would serve as a basis for future network traffic congestion control.

Integrated Off-Ramp Control Model for Freeway Traffic Management

Abstract: This study presents a mixed integer model for an integrated control between off-ramp and arterial traffic flows. The proposed study intends to minimize the queue spillback from off-ramp to the freeway mainline that may significantly degrade the performance quality of the entire freeway system. In this study, the Cell Transmission Model is employed to capture the traffic propagation on both freeway an surface streets, and to capture the interactions between those two types of flows within the target control boundaries. An efficient solution method based on Genetic Algorithm is provided along with a numeric case study to demonstrate the benefit of this proposed model.

Exploiting probe data to estimate the queue profile in urban networks

Abstract: Queues at signalized intersections are one of the main causes of traffic delays and urban traffic state variability. Hence, a method to estimate queue characteristics provides a better understanding of urban traffic dynamics and a performance measurement of signalized arterials. In order to capture the evolution of queues, we aim at leveraging the collective effect of spatially and temporally dispersed probe data to identify the formation and dissipation of queues in the time-space plane. The queue profile characterizes the evolution of both queue front and back, which consequently can be separated in a two-step estimation process resulting to the queue profile polygon. The evolution of queue front, in the time-space diagram, based on the kinematic traffic shockwave theory is modeled as a line with the known slope of queue-discharging shockwave and estimated with a constrained optimization and a technique known as support vector machine. The evolution of back of queue is more challenging and modeled as a piecewise linear function where slope of segments is between the queue-discharging shockwave and zero. In the proposed method, the input data consists of position and velocity of probe vehicles. The queue profile estimation method does not require any explicit information of signal settings and arrival distribution. The proposed method is tested with various penetration rates and sampling intervals of probe data, which reveals promising results once compared to a uniform arrival queue profile estimation procedure. The proposed method could be beneficial for spillback identification, vehicle trajectory construction, and fuel consumption and emission estimation.